A Gain-of-Function Mutation in a Plant Disease Resistance Gene Leads to Constitutive Activation of Downstream Signal Transduction Pathways in <i>suppressor of npr1-1</i> , <i>constitutive 1</i>
Bibliographic record
Abstract
Plants have evolved sophisticated defense mechanisms against pathogen infections, during which resistance (R) genes play central roles in recognizing pathogens and initiating defense cascades. Most of the cloned R genes share two common domains: the central domain, which encodes a nucleotide binding adaptor shared by APAF-1, certain R proteins, and CED-4 (NB-ARC), plus a C-terminal region that encodes Leu-rich repeats (LRR). In Arabidopsis, a dominant mutant, suppressor of npr1-1, constitutive 1 (snc1), was identified previously that constitutively expresses pathogenesis-related (PR) genes and resistance against both Pseudomonas syringae pv maculicola ES4326 and Peronospora parasitica Noco2. The snc1 mutation was mapped to the RPP4 cluster. In snc1, one of the TIR-NB-LRR-type R genes contains a point mutation that results in a single amino acid change from Glu to Lys in the region between NB-ARC and LRR. Deletions of this R gene in snc1 reverted the plants to wild-type morphology and completely abolished constitutive PR gene expression and disease resistance. The constitutive activation of the defense responses was not the result of the overexpression of the R gene, because its expression level was not altered in snc1. Our data suggest that the point mutation in snc1 renders the R gene constitutively active without interaction with pathogens. To analyze signal transduction pathways downstream of snc1, epistasis analyses between snc1 and pad4-1 or eds5-3 were performed. Although the resistance signaling in snc1 was fully dependent on PAD4, it was only partially affected by blocking salicylic acid (SA) synthesis, suggesting that snc1 activates both SA-dependent and SA-independent resistance pathways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".